Measures of acutance and shape for classification of breast tumors

Abstract

Most benign breast tumors possess well-defined, sharp boundaries that delineate them from surrounding tissues, as opposed to malignant tumors. Computer techniques proposed to date for tumor analysis have concentrated on shape factors of tumor regions and texture measures. While shape measures based on contours of tumor regions can indicate differences in shape complexities between circumscribed and spiculated tumors, they are not designed to characterize the density variations across the boundary of a tumor. In this paper we propose a region-based measure of image edge profile acutance which characterizes the transition in density of a region of interest (ROI) along normals to the ROI at every boundary pixel. We investigate the potential of acutance in quantifying the sharpness of the boundaries of tumors, and propose its application to discriminate between benign and malignant mammographic tumors. In addition, we study the complementary use of various shape factors based upon the shape of the ROI, such as compactness, Fourier descriptors, moments, and chord-length statistics to distinguish between circumscribed and spiculated tumors. Thirty-nine images from the Mammographic Image Analysis Society (MIAS) database and an additional set of 15 local cases were selected for this study. The cases included 16 circumscribed benign, seven circumscribed malignant, 12 spiculated benign, and 19 spiculated malignant lesions. All diagnoses were proven by pathologic examinations of resected tissue. The contours of the lesions were first marked by an expert radiologist using X-Paint and X-Windows on a SUN-SPARCstation 2 Workstation. For computation of acutance, the ROI boundaries were iteratively approximated using a split/merge and end-point adjustment technique to obtain the best-fitting polygonal approximation. The jackknife method using the Mahalanobis distance measure in the BMDP (Biomedical Programs) package was used for classification of the lesions using acutance and the shape factors as features in various combinations. Acutance alone resulted in a benign/malignant classification accuracy of 95% the MIAS cases. Compactness alone gave a circumscribed/spiculated classification rate of 92.3% with the MIAS cases. Acutance in combination with a moment-based shape measure and a Fourier descriptor-based measure gave four-group classification rate of 95% with the MIAS cases. The results indicate the importance of including lesion edge definition with shape information for classification of tumors, and that the proposed measure of acutance fills this need.

A computer-aided system for malignancy risk assessment of nodules in thyroid US images based on boundary features

“…Other boundary features applied in medical image analysis include the normalized radial length (NRG) [19], the area ratio [20], the fractal dimension (FD) [21], [31], the roughness index, the spiculation index, and the fractional concavity [23]. In addition, the extraction of boundary features by analyzing spectral domain, as it is the case with Fourier descriptor or wavelet descriptor, has been proved to provide noise-robust boundary representation in various applications [31].…”

Classification of breast masses using a committee machine of artificial neural networks

“…1͑d͔͒. 8,9,13,24 However, some benign masses can have lobulated or spiculated contours and blurred margins, and some maglignant tumors can have contours that are round or oval, as well as well-defined margins ͑see Fig. 1͒.…”

“…1͒. 8,9,13,24 To represent the features of breast masses as described before, several researchers have been studying image processing techniques to extract measures of shape, texture, and edge sharpness. 9-11,13,14,24 Quantitative features as described before have provided significant results in the discrimination between malignant tumors and benign masses in pattern classification studies.…”

“…With the objective to detect and extract the characteristics of lesions, several techniques of image processing have been proposed. [8][9][10][11][12][13][14][15] The artificial neural network ͑ANN͒ is an example of computational intelligence techniques that has been used to classify tumors related to breast cancer. 12,[16][17][18][19][20][21][22][23] Several types of network architecture, such as the multilayer perceptron ͑MLP͒, the single-layer perceptron ͑SLP͒, 19 and ra-dial basis functions ͑RBFs͒ 20 have been used for the classification of breast masses and tumors based on measures of shape, texture, and edge sharpness.…”

Two-dimensional microwave tomographic imaging of breast tissues

“…Some benign tumors too respond to microwaves similar to that of malignant tumors (Rangayyan et al, 1997). However, characterizing and analyzing such benign turnors is not considered in this study.…”